Overview

Dataset statistics

Number of variables19
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory156.0 B

Variable types

DateTime1
Numeric17
Categorical1

Alerts

civil_conflicts is highly correlated with state_intervention and 14 other fieldsHigh correlation
state_intervention is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with civil_conflicts and 1 other fieldsHigh correlation
gdp_ger is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_fra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_ita is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_jpn is highly correlated with gdp_ger and 11 other fieldsHigh correlation
gdp_can is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_rus is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_usa is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_gbr is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_bra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_ind is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_mex is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_zaf is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_chn is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_wld is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
civil_conflicts is highly correlated with gdp_ger and 12 other fieldsHigh correlation
state_intervention is highly correlated with total and 12 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with state_intervention and 1 other fieldsHigh correlation
gdp_ger is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_fra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_ita is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_jpn is highly correlated with civil_conflicts and 12 other fieldsHigh correlation
gdp_can is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_rus is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_usa is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_gbr is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_bra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_ind is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_mex is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_zaf is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gdp_chn is highly correlated with state_intervention and 12 other fieldsHigh correlation
gdp_wld is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
civil_conflicts is highly correlated with gdp_can and 5 other fieldsHigh correlation
state_intervention is highly correlated with gdp_ger and 9 other fieldsHigh correlation
gdp_ger is highly correlated with state_intervention and 13 other fieldsHigh correlation
gdp_fra is highly correlated with gdp_ger and 12 other fieldsHigh correlation
gdp_ita is highly correlated with gdp_ger and 11 other fieldsHigh correlation
gdp_jpn is highly correlated with gdp_ger and 3 other fieldsHigh correlation
gdp_can is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_rus is highly correlated with gdp_ger and 10 other fieldsHigh correlation
gdp_usa is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_gbr is highly correlated with state_intervention and 11 other fieldsHigh correlation
gdp_bra is highly correlated with state_intervention and 13 other fieldsHigh correlation
gdp_ind is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_mex is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_zaf is highly correlated with state_intervention and 13 other fieldsHigh correlation
gdp_chn is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gdp_wld is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
year is highly correlated with civil_conflicts and 17 other fieldsHigh correlation
civil_conflicts is highly correlated with year and 5 other fieldsHigh correlation
state_intervention is highly correlated with year and 9 other fieldsHigh correlation
conflict_between_states is highly correlated with year and 4 other fieldsHigh correlation
total is highly correlated with year and 7 other fieldsHigh correlation
gdp_ger is highly correlated with year and 13 other fieldsHigh correlation
gdp_fra is highly correlated with year and 14 other fieldsHigh correlation
gdp_ita is highly correlated with year and 10 other fieldsHigh correlation
gdp_jpn is highly correlated with year and 7 other fieldsHigh correlation
gdp_can is highly correlated with year and 12 other fieldsHigh correlation
gdp_rus is highly correlated with year and 12 other fieldsHigh correlation
gdp_usa is highly correlated with year and 17 other fieldsHigh correlation
gdp_gbr is highly correlated with year and 14 other fieldsHigh correlation
gdp_bra is highly correlated with year and 15 other fieldsHigh correlation
gdp_ind is highly correlated with year and 14 other fieldsHigh correlation
gdp_mex is highly correlated with year and 13 other fieldsHigh correlation
gdp_zaf is highly correlated with year and 14 other fieldsHigh correlation
gdp_chn is highly correlated with year and 14 other fieldsHigh correlation
gdp_wld is highly correlated with year and 14 other fieldsHigh correlation
year has unique values Unique
gdp_ger has unique values Unique
gdp_fra has unique values Unique
gdp_ita has unique values Unique
gdp_jpn has unique values Unique
gdp_can has unique values Unique
gdp_rus has unique values Unique
gdp_usa has unique values Unique
gdp_gbr has unique values Unique
gdp_bra has unique values Unique
gdp_ind has unique values Unique
gdp_mex has unique values Unique
gdp_zaf has unique values Unique
gdp_chn has unique values Unique
gdp_wld has unique values Unique

Reproduction

Analysis started2022-07-04 11:39:17.020622
Analysis finished2022-07-04 11:39:46.910830
Duration29.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
Minimum1970-01-01 00:00:00.000001
Maximum1970-01-01 00:00:00.000002
2022-07-04T13:39:47.005852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:47.114876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

civil_conflicts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.21875
Minimum23
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:47.220901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile25.2
Q128
median31
Q334.25
95-th percentile44.9
Maximum48
Range25
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.121007586
Coefficient of variation (CV)0.1899827767
Kurtosis1.045391895
Mean32.21875
Median Absolute Deviation (MAD)3
Skewness1.153251821
Sum1031
Variance37.46673387
MonotonicityNot monotonic
2022-07-04T13:39:47.326924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
324
12.5%
284
12.5%
293
9.4%
273
9.4%
313
9.4%
303
9.4%
442
 
6.2%
372
 
6.2%
232
 
6.2%
331
 
3.1%
Other values (5)5
15.6%
ValueCountFrequency (%)
232
6.2%
273
9.4%
284
12.5%
293
9.4%
303
9.4%
313
9.4%
324
12.5%
331
 
3.1%
341
 
3.1%
351
 
3.1%
ValueCountFrequency (%)
481
 
3.1%
461
 
3.1%
442
6.2%
372
6.2%
361
 
3.1%
351
 
3.1%
341
 
3.1%
331
 
3.1%
324
12.5%
313
9.4%

state_intervention
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.25
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:47.424947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5.5
Q39
95-th percentile22.8
Maximum25
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.900210375
Coefficient of variation (CV)0.8363891364
Kurtosis0.755369208
Mean8.25
Median Absolute Deviation (MAD)2.5
Skewness1.413726976
Sum264
Variance47.61290323
MonotonicityNot monotonic
2022-07-04T13:39:47.634994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
45
15.6%
54
12.5%
34
12.5%
23
9.4%
63
9.4%
73
9.4%
92
 
6.2%
252
 
6.2%
81
 
3.1%
131
 
3.1%
Other values (4)4
12.5%
ValueCountFrequency (%)
23
9.4%
34
12.5%
45
15.6%
54
12.5%
63
9.4%
73
9.4%
81
 
3.1%
92
 
6.2%
131
 
3.1%
181
 
3.1%
ValueCountFrequency (%)
252
6.2%
211
 
3.1%
201
 
3.1%
191
 
3.1%
181
 
3.1%
131
 
3.1%
92
6.2%
81
 
3.1%
73
9.4%
63
9.4%

conflict_between_states
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size384.0 B
2
12 
1
10 
0
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Length

2022-07-04T13:39:47.732016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T13:39:47.841040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring characters

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.625
Minimum31
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:47.935062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile32
Q135.75
median40
Q349
95-th percentile54.45
Maximum56
Range25
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.732420228
Coefficient of variation (CV)0.1857638493
Kurtosis-1.076805083
Mean41.625
Median Absolute Deviation (MAD)7
Skewness0.4612043776
Sum1332
Variance59.79032258
MonotonicityNot monotonic
2022-07-04T13:39:48.037085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
405
15.6%
334
12.5%
383
 
9.4%
523
 
9.4%
492
 
6.2%
322
 
6.2%
411
 
3.1%
391
 
3.1%
481
 
3.1%
431
 
3.1%
Other values (9)9
28.1%
ValueCountFrequency (%)
311
 
3.1%
322
 
6.2%
334
12.5%
351
 
3.1%
361
 
3.1%
371
 
3.1%
383
9.4%
391
 
3.1%
405
15.6%
411
 
3.1%
ValueCountFrequency (%)
561
 
3.1%
551
 
3.1%
541
 
3.1%
523
9.4%
511
 
3.1%
492
6.2%
481
 
3.1%
431
 
3.1%
421
 
3.1%
411
 
3.1%

gdp_ger
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.856804375 × 1012
Minimum1.39897 × 1012
Maximum3.97729 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:48.137107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.39897 × 1012
5-th percentile1.825174 × 1012
Q12.179105 × 1012
median2.830605 × 1012
Q33.5680675 × 1012
95-th percentile3.888672 × 1012
Maximum3.97729 × 1012
Range2.57832 × 1012
Interquartile range (IQR)1.3889625 × 1012

Descriptive statistics

Standard deviation7.851466882 × 1011
Coefficient of variation (CV)0.2748339001
Kurtosis-1.478926327
Mean2.856804375 × 1012
Median Absolute Deviation (MAD)6.97785 × 1011
Skewness-0.0676526483
Sum9.141774 × 1013
Variance6.164553219 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:48.248132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.39897 × 10121
 
3.1%
1.77167 × 10121
 
3.1%
3.88833 × 10121
 
3.1%
3.97729 × 10121
 
3.1%
3.69085 × 10121
 
3.1%
3.46985 × 10121
 
3.1%
3.35759 × 10121
 
3.1%
3.88909 × 10121
 
3.1%
3.7338 × 10121
 
3.1%
3.52714 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.39897 × 10121
3.1%
1.77167 × 10121
3.1%
1.86895 × 10121
3.1%
1.94579 × 10121
3.1%
1.94798 × 10121
3.1%
2.07132 × 10121
3.1%
2.07848 × 10121
3.1%
2.13157 × 10121
3.1%
2.19495 × 10121
3.1%
2.20507 × 10121
3.1%
ValueCountFrequency (%)
3.97729 × 10121
3.1%
3.88909 × 10121
3.1%
3.88833 × 10121
3.1%
3.84641 × 10121
3.1%
3.74931 × 10121
3.1%
3.74526 × 10121
3.1%
3.7338 × 10121
3.1%
3.69085 × 10121
3.1%
3.52714 × 10121
3.1%
3.46985 × 10121
3.1%

gdp_fra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.058603438 × 1012
Minimum1.02521 × 1012
Maximum2.9303 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:48.356157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.02521 × 1012
5-th percentile1.269235 × 1012
Q11.4400275 × 1012
median2.15829 × 1012
Q32.66636 × 1012
95-th percentile2.8601 × 1012
Maximum2.9303 × 1012
Range1.90509 × 1012
Interquartile range (IQR)1.2263325 × 1012

Descriptive statistics

Standard deviation6.3679007 × 1011
Coefficient of variation (CV)0.309331102
Kurtosis-1.745112322
Mean2.058603438 × 1012
Median Absolute Deviation (MAD)6.4313 × 1011
Skewness-0.05655863174
Sum6.587531 × 1013
Variance4.055015932 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:48.470182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.02521 × 10121
 
3.1%
1.26918 × 10121
 
3.1%
2.72887 × 10121
 
3.1%
2.79096 × 10121
 
3.1%
2.59515 × 10121
 
3.1%
2.47296 × 10121
 
3.1%
2.43919 × 10121
 
3.1%
2.85596 × 10121
 
3.1%
2.81188 × 10121
 
3.1%
2.68367 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.02521 × 10121
3.1%
1.26918 × 10121
3.1%
1.26928 × 10121
3.1%
1.32282 × 10121
3.1%
1.36564 × 10121
3.1%
1.37766 × 10121
3.1%
1.39398 × 10121
3.1%
1.40147 × 10121
3.1%
1.45288 × 10121
3.1%
1.49315 × 10121
3.1%
ValueCountFrequency (%)
2.9303 × 10121
3.1%
2.86516 × 10121
3.1%
2.85596 × 10121
3.1%
2.81188 × 10121
3.1%
2.79096 × 10121
3.1%
2.72887 × 10121
3.1%
2.70089 × 10121
3.1%
2.68367 × 10121
3.1%
2.66059 × 10121
3.1%
2.64519 × 10121
3.1%

gdp_ita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.662015969 × 1012
Minimum9.28661 × 1011
Maximum2.40866 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:48.578207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.28661 × 1011
5-th percentile1.083803 × 1012
Q11.245135 × 1012
median1.82159 × 1012
Q32.0882025 × 1012
95-th percentile2.2499505 × 1012
Maximum2.40866 × 1012
Range1.479999 × 1012
Interquartile range (IQR)8.430675 × 1011

Descriptive statistics

Standard deviation4.528428629 × 1011
Coefficient of variation (CV)0.272466012
Kurtosis-1.603179803
Mean1.662015969 × 1012
Median Absolute Deviation (MAD)4.32455 × 1011
Skewness-0.02371028472
Sum5.3184511 × 1013
Variance2.050666585 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:48.690232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9.28661 × 10111
 
3.1%
1.18122 × 10121
 
3.1%
2.00938 × 10121
 
3.1%
2.09193 × 10121
 
3.1%
1.9618 × 10121
 
3.1%
1.87707 × 10121
 
3.1%
1.83664 × 10121
 
3.1%
2.16201 × 10121
 
3.1%
2.14192 × 10121
 
3.1%
2.08696 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
9.28661 × 10111
3.1%
1.06496 × 10121
3.1%
1.09922 × 10121
3.1%
1.14668 × 10121
3.1%
1.16802 × 10121
3.1%
1.17466 × 10121
3.1%
1.18122 × 10121
3.1%
1.24188 × 10121
3.1%
1.24622 × 10121
3.1%
1.25245 × 10121
3.1%
ValueCountFrequency (%)
2.40866 × 10121
3.1%
2.29499 × 10121
3.1%
2.2131 × 10121
3.1%
2.19993 × 10121
3.1%
2.16201 × 10121
3.1%
2.14192 × 10121
3.1%
2.1361 × 10121
3.1%
2.09193 × 10121
3.1%
2.08696 × 10121
3.1%
2.00938 × 10121
3.1%

gdp_jpn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.755439688 × 1012
Minimum3.05491 × 1012
Maximum6.27236 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:48.803258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.05491 × 1012
5-th percentile3.3812 × 1012
Q14.4518375 × 1012
median4.895055 × 1012
Q35.06999 × 1012
95-th percentile5.972406 × 1012
Maximum6.27236 × 1012
Range3.21745 × 1012
Interquartile range (IQR)6.181525 × 1011

Descriptive statistics

Standard deviation7.234982785 × 1011
Coefficient of variation (CV)0.1521411953
Kurtosis0.9287565028
Mean4.755439688 × 1012
Median Absolute Deviation (MAD)3.46385 × 1011
Skewness-0.3043462343
Sum1.5217407 × 1014
Variance5.234497589 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:48.911282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.05491 × 10121
 
3.1%
3.13282 × 10121
 
3.1%
5.14878 × 10121
 
3.1%
5.03689 × 10121
 
3.1%
4.93084 × 10121
 
3.1%
5.00368 × 10121
 
3.1%
4.44493 × 10121
 
3.1%
4.89699 × 10121
 
3.1%
5.21233 × 10121
 
3.1%
6.27236 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.05491 × 10121
3.1%
3.13282 × 10121
3.1%
3.58442 × 10121
3.1%
3.90881 × 10121
3.1%
4.09836 × 10121
3.1%
4.18285 × 10121
3.1%
4.37471 × 10121
3.1%
4.44493 × 10121
3.1%
4.45414 × 10121
3.1%
4.49245 × 10121
3.1%
ValueCountFrequency (%)
6.27236 × 10121
3.1%
6.23315 × 10121
3.1%
5.75907 × 10121
3.1%
5.54556 × 10121
3.1%
5.28949 × 10121
3.1%
5.21233 × 10121
3.1%
5.14878 × 10121
3.1%
5.10668 × 10121
3.1%
5.05776 × 10121
3.1%
5.03689 × 10121
3.1%

gdp_can
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.140948875 × 1012
Minimum5.65056 × 1011
Maximum1.8466 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:49.014305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.65056 × 1011
5-th percentile5.777034 × 1011
Q16.326365 × 1011
median1.0999 × 1012
Q31.62436 × 1012
95-th percentile1.815929 × 1012
Maximum1.8466 × 1012
Range1.281544 × 1012
Interquartile range (IQR)9.917235 × 1011

Descriptive statistics

Standard deviation4.954701262 × 1011
Coefficient of variation (CV)0.4342614617
Kurtosis-1.793269796
Mean1.140948875 × 1012
Median Absolute Deviation (MAD)4.80463 × 1011
Skewness0.1208825754
Sum3.6510364 × 1013
Variance2.45490646 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:49.122330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.65056 × 10111
 
3.1%
5.9393 × 10111
 
3.1%
1.74202 × 10121
 
3.1%
1.72533 × 10121
 
3.1%
1.64927 × 10121
 
3.1%
1.52799 × 10121
 
3.1%
1.55651 × 10121
 
3.1%
1.80575 × 10121
 
3.1%
1.8466 × 10121
 
3.1%
1.82837 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
5.65056 × 10111
3.1%
5.77171 × 10111
3.1%
5.78139 × 10111
3.1%
5.92388 × 10111
3.1%
5.9393 × 10111
3.1%
6.04032 × 10111
3.1%
6.10328 × 10111
3.1%
6.28546 × 10111
3.1%
6.34 × 10111
3.1%
6.54987 × 10111
3.1%
ValueCountFrequency (%)
1.8466 × 10121
3.1%
1.82837 × 10121
3.1%
1.80575 × 10121
3.1%
1.79333 × 10121
3.1%
1.74202 × 10121
3.1%
1.72533 × 10121
3.1%
1.64927 × 10121
3.1%
1.64542 × 10121
3.1%
1.61734 × 10121
3.1%
1.55651 × 10121
3.1%

gdp_rus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.854589688 × 1011
Minimum1.95907 × 1011
Maximum2.29247 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:49.227354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.95907 × 1011
5-th percentile2.6589475 × 1011
Q14.02581 × 1011
median6.77517 × 1011
Q31.53724 × 1012
95-th percentile2.126317 × 1012
Maximum2.29247 × 1012
Range2.096563 × 1012
Interquartile range (IQR)1.134659 × 1012

Descriptive statistics

Standard deviation6.67838854 × 1011
Coefficient of variation (CV)0.6776932121
Kurtosis-1.161380488
Mean9.854589688 × 1011
Median Absolute Deviation (MAD)4.121845 × 1011
Skewness0.5264135059
Sum3.1534687 × 1013
Variance4.460087348 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:49.331377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.065 × 10111
 
3.1%
5.16814 × 10111
 
3.1%
1.68745 × 10121
 
3.1%
1.65733 × 10121
 
3.1%
1.5742 × 10121
 
3.1%
1.27679 × 10121
 
3.1%
1.36348 × 10121
 
3.1%
2.05924 × 10121
 
3.1%
2.29247 × 10121
 
3.1%
2.2083 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.95907 × 10111
3.1%
2.5971 × 10111
3.1%
2.70955 × 10111
3.1%
3.06602 × 10111
3.1%
3.4547 × 10111
3.1%
3.91725 × 10111
3.1%
3.95077 × 10111
3.1%
3.95537 × 10111
3.1%
4.04929 × 10111
3.1%
4.30348 × 10111
3.1%
ValueCountFrequency (%)
2.29247 × 10121
3.1%
2.2083 × 10121
3.1%
2.05924 × 10121
3.1%
2.04593 × 10121
3.1%
1.68745 × 10121
3.1%
1.66085 × 10121
3.1%
1.65733 × 10121
3.1%
1.5742 × 10121
3.1%
1.52492 × 10121
3.1%
1.4835 × 10121
3.1%

gdp_usa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.274650875 × 1013
Minimum5.64158 × 1012
Maximum2.14332 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:49.535423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.64158 × 1012
5-th percentile6.0703845 × 1012
Q18.4514425 × 1012
median1.262515 × 1013
Q31.634395 × 1013
95-th percentile2.0765395 × 1013
Maximum2.14332 × 1013
Range1.579162 × 1013
Interquartile range (IQR)7.8925075 × 1012

Descriptive statistics

Standard deviation4.88471814 × 1012
Coefficient of variation (CV)0.3832200829
Kurtosis-1.154358731
Mean1.274650875 × 1013
Median Absolute Deviation (MAD)4.103625 × 1012
Skewness0.1982472094
Sum4.0788828 × 1014
Variance2.386047131 × 1025
MonotonicityNot monotonic
2022-07-04T13:39:49.643448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.64158 × 10121
 
3.1%
5.96314 × 10121
 
3.1%
2.14332 × 10131
 
3.1%
2.06119 × 10131
 
3.1%
1.9543 × 10131
 
3.1%
1.87451 × 10131
 
3.1%
1.82383 × 10131
 
3.1%
1.75272 × 10131
 
3.1%
1.67848 × 10131
 
3.1%
1.6197 × 10131
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
5.64158 × 10121
3.1%
5.96314 × 10121
3.1%
6.15813 × 10121
3.1%
6.52033 × 10121
3.1%
6.85856 × 10121
3.1%
7.28724 × 10121
3.1%
7.63975 × 10121
3.1%
8.07312 × 10121
3.1%
8.57755 × 10121
3.1%
9.06282 × 10121
3.1%
ValueCountFrequency (%)
2.14332 × 10131
3.1%
2.0953 × 10131
3.1%
2.06119 × 10131
3.1%
1.9543 × 10131
3.1%
1.87451 × 10131
3.1%
1.82383 × 10131
3.1%
1.75272 × 10131
3.1%
1.67848 × 10131
3.1%
1.6197 × 10131
3.1%
1.55426 × 10131
3.1%

gdp_gbr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.131441719 × 1012
Minimum9.26885 × 1011
Maximum3.10618 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:49.751472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.26885 × 1011
5-th percentile1.078869 × 1012
Q11.525055 × 1012
median2.423805 × 1012
Q32.7320875 × 1012
95-th percentile3.01534 × 1012
Maximum3.10618 × 1012
Range2.179295 × 1012
Interquartile range (IQR)1.2070325 × 1012

Descriptive statistics

Standard deviation7.255133834 × 1011
Coefficient of variation (CV)0.340386217
Kurtosis-1.543218733
Mean2.131441719 × 1012
Median Absolute Deviation (MAD)5.86245 × 1011
Skewness-0.2657441889
Sum6.8206135 × 1013
Variance5.263696695 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:49.855495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9.26885 × 10111
 
3.1%
1.09317 × 10121
 
3.1%
2.87867 × 10121
 
3.1%
2.90079 × 10121
 
3.1%
2.69902 × 10121
 
3.1%
2.72285 × 10121
 
3.1%
2.95657 × 10121
 
3.1%
3.08717 × 10121
 
3.1%
2.80329 × 10121
 
3.1%
2.71916 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
9.26885 × 10111
3.1%
1.06139 × 10121
3.1%
1.09317 × 10121
3.1%
1.14049 × 10121
3.1%
1.1428 × 10121
3.1%
1.17966 × 10121
3.1%
1.34642 × 10121
3.1%
1.42151 × 10121
3.1%
1.55957 × 10121
3.1%
1.64391 × 10121
3.1%
ValueCountFrequency (%)
3.10618 × 10121
3.1%
3.08717 × 10121
3.1%
2.95657 × 10121
3.1%
2.93888 × 10121
3.1%
2.90079 × 10121
3.1%
2.87867 × 10121
3.1%
2.80329 × 10121
3.1%
2.7598 × 10121
3.1%
2.72285 × 10121
3.1%
2.71916 × 10121
3.1%

gdp_bra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.221890594 × 1012
Minimum3.28188 × 1011
Maximum2.61616 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:49.956518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.28188 × 1011
5-th percentile3.4503945 × 1011
Q15.595465 × 1011
median8.8742 × 1011
Q31.8211125 × 1012
95-th percentile2.4686455 × 1012
Maximum2.61616 × 1012
Range2.287972 × 1012
Interquartile range (IQR)1.261566 × 1012

Descriptive statistics

Standard deviation7.554424606 × 1011
Coefficient of variation (CV)0.6182570391
Kurtosis-1.236598509
Mean1.221890594 × 1012
Median Absolute Deviation (MAD)5.29758 × 1011
Skewness0.466283602
Sum3.9100499 × 1013
Variance5.706933113 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:50.061542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.47028 × 10111
 
3.1%
3.90726 × 10111
 
3.1%
1.87782 × 10121
 
3.1%
1.91693 × 10121
 
3.1%
2.06351 × 10121
 
3.1%
1.79569 × 10121
 
3.1%
1.80221 × 10121
 
3.1%
2.45604 × 10121
 
3.1%
2.47282 × 10121
 
3.1%
2.46523 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.28188 × 10111
3.1%
3.42609 × 10111
3.1%
3.47028 × 10111
3.1%
3.68296 × 10111
3.1%
3.90726 × 10111
3.1%
5.09795 × 10111
3.1%
5.2537 × 10111
3.1%
5.58234 × 10111
3.1%
5.59984 × 10111
3.1%
5.99642 × 10111
3.1%
ValueCountFrequency (%)
2.61616 × 10121
3.1%
2.47282 × 10121
3.1%
2.46523 × 10121
3.1%
2.45604 × 10121
3.1%
2.20884 × 10121
3.1%
2.06351 × 10121
3.1%
1.91693 × 10121
3.1%
1.87782 × 10121
3.1%
1.80221 × 10121
3.1%
1.79569 × 10121
3.1%

gdp_ind
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.144962438 × 1012
Minimum2.70105 × 1011
Maximum2.8705 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:50.162565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.70105 × 1011
5-th percentile2.841976 × 1011
Q14.1012525 × 1011
median7.647655 × 1011
Q31.83491 × 1012
95-th percentile2.678637 × 1012
Maximum2.8705 × 1012
Range2.600395 × 1012
Interquartile range (IQR)1.42478475 × 1012

Descriptive statistics

Standard deviation8.744778315 × 1011
Coefficient of variation (CV)0.7637611531
Kurtosis-1.008517482
Mean1.144962438 × 1012
Median Absolute Deviation (MAD)4.60349 × 1011
Skewness0.6832227337
Sum3.6638798 × 1013
Variance7.647114778 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:50.270589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.96042 × 10111
 
3.1%
3.20979 × 10111
 
3.1%
2.8705 × 10121
 
3.1%
2.70111 × 10121
 
3.1%
2.65147 × 10121
 
3.1%
2.2948 × 10121
 
3.1%
2.10359 × 10121
 
3.1%
2.03913 × 10121
 
3.1%
1.85672 × 10121
 
3.1%
1.82764 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.70105 × 10111
3.1%
2.79296 × 10111
3.1%
2.88208 × 10111
3.1%
2.96042 × 10111
3.1%
3.20979 × 10111
3.1%
3.27276 × 10111
3.1%
3.60282 × 10111
3.1%
3.92897 × 10111
3.1%
4.15868 × 10111
3.1%
4.21351 × 10111
3.1%
ValueCountFrequency (%)
2.8705 × 10121
3.1%
2.70111 × 10121
3.1%
2.66025 × 10121
3.1%
2.65147 × 10121
3.1%
2.2948 × 10121
3.1%
2.10359 × 10121
3.1%
2.03913 × 10121
3.1%
1.85672 × 10121
3.1%
1.82764 × 10121
3.1%
1.82305 × 10121
3.1%

gdp_mex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.204311562 × 1011
Minimum2.21401 × 1011
Maximum1.31535 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:50.378614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.21401 × 1011
5-th percentile2.8979295 × 1011
Q15.200605 × 1011
median8.298585 × 1011
Q31.12222 × 1012
95-th percentile1.2716845 × 1012
Maximum1.31535 × 1012
Range1.093949 × 1012
Interquartile range (IQR)6.021595 × 1011

Descriptive statistics

Standard deviation3.434617405 × 1011
Coefficient of variation (CV)0.4186356623
Kurtosis-1.328590313
Mean8.204311562 × 1011
Median Absolute Deviation (MAD)3.16204 × 1011
Skewness-0.2284284434
Sum2.6253797 × 1013
Variance1.179659672 × 1023
MonotonicityNot monotonic
2022-07-04T13:39:50.484638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.21401 × 10111
 
3.1%
2.61254 × 10111
 
3.1%
1.26943 × 10121
 
3.1%
1.22241 × 10121
 
3.1%
1.15891 × 10121
 
3.1%
1.07849 × 10121
 
3.1%
1.17187 × 10121
 
3.1%
1.31535 × 10121
 
3.1%
1.27444 × 10121
 
3.1%
1.20109 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.21401 × 10111
3.1%
2.61254 × 10111
3.1%
3.13143 × 10111
3.1%
3.60074 × 10111
3.1%
3.63158 × 10111
3.1%
4.10976 × 10111
3.1%
5.00413 × 10111
3.1%
5.00736 × 10111
3.1%
5.26502 × 10111
3.1%
5.27813 × 10111
3.1%
ValueCountFrequency (%)
1.31535 × 10121
3.1%
1.27444 × 10121
3.1%
1.26943 × 10121
3.1%
1.22241 × 10121
3.1%
1.20109 × 10121
3.1%
1.18049 × 10121
3.1%
1.17187 × 10121
3.1%
1.15891 × 10121
3.1%
1.10999 × 10121
3.1%
1.07849 × 10121
3.1%

gdp_zaf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.59219808 × 1011
Minimum9.903085682 × 1010
Maximum4.58202 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:50.589661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.903085682 × 1010
5-th percentile1.2016705 × 1011
Q11.51694 × 1011
median2.723375 × 1011
Q33.5533225 × 1011
95-th percentile4.250312 × 1011
Maximum4.58202 × 1011
Range3.591711432 × 1011
Interquartile range (IQR)2.0363825 × 1011

Descriptive statistics

Standard deviation1.159189402 × 1011
Coefficient of variation (CV)0.4471839596
Kurtosis-1.585321494
Mean2.59219808 × 1011
Median Absolute Deviation (MAD)1.17211 × 1011
Skewness0.1535474479
Sum8.295033857 × 1012
Variance1.343720069 × 1022
MonotonicityNot monotonic
2022-07-04T13:39:50.695685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9.903085682 × 10101
 
3.1%
1.15552 × 10111
 
3.1%
3.87935 × 10111
 
3.1%
4.04842 × 10111
 
3.1%
3.81449 × 10111
 
3.1%
3.23586 × 10111
 
3.1%
3.4671 × 10111
 
3.1%
3.81199 × 10111
 
3.1%
4.00886 × 10111
 
3.1%
4.34401 × 10111
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
9.903085682 × 10101
3.1%
1.15552 × 10111
3.1%
1.23943 × 10111
3.1%
1.29088 × 10111
3.1%
1.34545 × 10111
3.1%
1.3543 × 10111
3.1%
1.47197 × 10111
3.1%
1.51517 × 10111
3.1%
1.51753 × 10111
3.1%
1.52983 × 10111
3.1%
ValueCountFrequency (%)
4.58202 × 10111
3.1%
4.34401 × 10111
3.1%
4.17365 × 10111
3.1%
4.04842 × 10111
3.1%
4.00886 × 10111
3.1%
3.87935 × 10111
3.1%
3.81449 × 10111
3.1%
3.81199 × 10111
3.1%
3.4671 × 10111
3.1%
3.35442 × 10111
3.1%

gdp_chn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.776625 × 1012
Minimum3.47768 × 1011
Maximum1.47227 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:50.801709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.47768 × 1011
5-th percentile3.7324125 × 1011
Q19.3713975 × 1011
median2.12066 × 1012
Q38.791775 × 1012
95-th percentile1.4068095 × 1013
Maximum1.47227 × 1013
Range1.4374932 × 1013
Interquartile range (IQR)7.85463525 × 1012

Descriptive statistics

Standard deviation4.892493285 × 1012
Coefficient of variation (CV)1.024257354
Kurtosis-0.7776836837
Mean4.776625 × 1012
Median Absolute Deviation (MAD)1.7155155 × 1012
Skewness0.86284
Sum1.52852 × 1014
Variance2.393649055 × 1025
MonotonicityStrictly increasing
2022-07-04T13:39:50.907733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.47768 × 10111
 
3.1%
3.60858 × 10111
 
3.1%
1.42799 × 10131
 
3.1%
1.38948 × 10131
 
3.1%
1.23104 × 10131
 
3.1%
1.12333 × 10131
 
3.1%
1.10616 × 10131
 
3.1%
1.04757 × 10131
 
3.1%
9.57041 × 10121
 
3.1%
8.53223 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.47768 × 10111
3.1%
3.60858 × 10111
3.1%
3.83373 × 10111
3.1%
4.26916 × 10111
3.1%
4.44731 × 10111
3.1%
5.64325 × 10111
3.1%
7.34548 × 10111
3.1%
8.63747 × 10111
3.1%
9.61604 × 10111
3.1%
1.02904 × 10121
3.1%
ValueCountFrequency (%)
1.47227 × 10131
3.1%
1.42799 × 10131
3.1%
1.38948 × 10131
3.1%
1.23104 × 10131
3.1%
1.12333 × 10131
3.1%
1.10616 × 10131
3.1%
1.04757 × 10131
3.1%
9.57041 × 10121
3.1%
8.53223 × 10121
3.1%
7.5515 × 10121
3.1%

gdp_wld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.114072188 × 1013
Minimum2.01713 × 1013
Maximum8.75681 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:39:51.013757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.01713 × 1013
5-th percentile2.3271625 × 1013
Q13.1599775 × 1013
median4.59461 × 1013
Q37.5165725 × 1013
95-th percentile8.5434285 × 1013
Maximum8.75681 × 1013
Range6.73968 × 1013
Interquartile range (IQR)4.356595 × 1013

Descriptive statistics

Standard deviation2.290742399 × 1013
Coefficient of variation (CV)0.4479292265
Kurtosis-1.578065211
Mean5.114072188 × 1013
Median Absolute Deviation (MAD)1.91245 × 1013
Skewness0.2510165895
Sum1.6365031 × 1015
Variance5.24750074 × 1026
MonotonicityNot monotonic
2022-07-04T13:39:51.117781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.01713 × 10131
 
3.1%
2.27395 × 10131
 
3.1%
8.75681 × 10131
 
3.1%
8.62743 × 10131
 
3.1%
8.12246 × 10131
 
3.1%
7.63132 × 10131
 
3.1%
7.51171 × 10131
 
3.1%
7.95755 × 10131
 
3.1%
7.74432 × 10131
 
3.1%
7.53116 × 10131
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.01713 × 10131
3.1%
2.27395 × 10131
3.1%
2.3707 × 10131
3.1%
2.53937 × 10131
3.1%
2.58225 × 10131
3.1%
2.78724 × 10131
3.1%
3.10438 × 10131
3.1%
3.15397 × 10131
3.1%
3.16198 × 10131
3.1%
3.17363 × 10131
3.1%
ValueCountFrequency (%)
8.75681 × 10131
3.1%
8.62743 × 10131
3.1%
8.4747 × 10131
3.1%
8.12246 × 10131
3.1%
7.95755 × 10131
3.1%
7.74432 × 10131
3.1%
7.63132 × 10131
3.1%
7.53116 × 10131
3.1%
7.51171 × 10131
3.1%
7.36715 × 10131
3.1%

Interactions

2022-07-04T13:39:44.942385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:18.704347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.235693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.872063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.541441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.109795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.834184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.521673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.210055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.780410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.445787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.162174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.821549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.351895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.014271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.671646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.370029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.032406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:18.800369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.327714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.964084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.629461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.314842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.924206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.615695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.303076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.979455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.537807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.253195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.910569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.442916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.102291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.762666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.459050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.123426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:18.887388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.413734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.055104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.719480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.404862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.014225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.706715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.393096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.067475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.628828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.345216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.999590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.530936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.191311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.853687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.549070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.213446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:18.977409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.504754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.147126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.808501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.497883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.105353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.796736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.483117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.157495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.720848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.435236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.088610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.622956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.281331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.946708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.641091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.306467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.065429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.595775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.237144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.900521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.591904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.198375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.886756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.575137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.246515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.815870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.525257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.177630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.820001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.370352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.036728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.734112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.510513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.157450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.790819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.334168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.995543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.687926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.293396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.983778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.669159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.339536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.913892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.618277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.269651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.914022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.463372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.131750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.829133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.603534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.247470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.883840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.429188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.091565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.782946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.499442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.076799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.765181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.440559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.012914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.711298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.361671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.007043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.556393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.226771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.923155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.695555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.337490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.974860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.519209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.185586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.881970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.592463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.173821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.856201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.532580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.107936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.802319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.450692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.099064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.646414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.320792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.016176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.790576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.431512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.064881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.609229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.280607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.978991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.684484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.265841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.949222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.624601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.314983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.894340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.541712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.190085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.739435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.414814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.108196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.884598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.518532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.150900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.697250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.370628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.071012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.772504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.353861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.040242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.712620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.406003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.983360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.627732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.278105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:40.827455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.503834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.200217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:45.985621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.612553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.243921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.789271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.465649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.166033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.867525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.448883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.135264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.806642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.504026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.079381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.719753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.372126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.030501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.598855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.295239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.076641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.697571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.331941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:22.878290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.553670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.259055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:27.960546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.540904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.225284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.893662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.597047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.168402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.807772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.461146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.121521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.692877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.386259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.167662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.788592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.418960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.083337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.643689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.350076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.053567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.741949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.315305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:32.982682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.689067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.255421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.895792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.554167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.212542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.783897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.477280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.260683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.876612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.506980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.172357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.733710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.445097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.146588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.833970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.407326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.072702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.783089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.346442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:37.983812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.645188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.302562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.876918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.569300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.354704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:19.965632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.595001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.262378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.833733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.540118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.237609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:29.924991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.502347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.159722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.877110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.543487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.074833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.738209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.393583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:42.967939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.661321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.452726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.056653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.688021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.356398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:24.925754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.637140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.331630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.020012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.594368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.257745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:34.971131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.636508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.169854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.830230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.486604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.071962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.755342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:46.553749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:20.144673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:21.782043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:23.448419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:25.017775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:26.737163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:28.424651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:30.114033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:31.688389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:33.351765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:35.066152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:36.728528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:38.259875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:39.921250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:41.579625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:43.276008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:39:44.849364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T13:39:51.328829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T13:39:51.466860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T13:39:51.602890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T13:39:51.738921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-04T13:39:46.700783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-04T13:39:46.869821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

yearcivil_conflictsstate_interventionconflict_between_statestotalgdp_gergdp_fragdp_itagdp_jpngdp_cangdp_rusgdp_usagdp_gbrgdp_bragdp_indgdp_mexgdp_zafgdp_chngdp_wld
01970-01-01 00:00:00.00000198933524013989700000001025210000000928661000000305491000000056505600000050650000000056415800000009268850000003470280000002960420000002214010000009903085682534776800000020171300000000
11970-01-01 00:00:00.00000199044324917716700000001269180000000118122000000031328200000005939300000005168140000005963140000000109317000000039072600000032097900000026125400000011555200000036085800000022739500000000
21970-01-01 00:00:00.00000199148225218689500000001269280000000124622000000035844200000006103280000005179630000006158130000000114280000000034260900000027010500000031314300000012394300000038337300000023707000000000
31970-01-01 00:00:00.00000199244414921315700000001401470000000132016000000039088100000005923880000004602910000006520330000000117966000000032818800000028820800000036315800000013454500000042691600000025393700000000
41970-01-01 00:00:00.00000199337604320713200000001322820000000106496000000044541400000005771710000004350840000006858560000000106139000000036829600000027929600000050073600000014719700000044473100000025822500000000
51970-01-01 00:00:00.00000199446204822050700000001393980000000109922000000049988000000005781390000003950770000007287240000000114049000000052537000000032727600000052781300000015351300000056432500000027872400000000
61970-01-01 00:00:00.00000199537214025857900000001601090000000117466000000055455600000006040320000003955370000007639750000000134642000000076933300000036028200000036007400000017173500000073454800000031043800000000
71970-01-01 00:00:00.00000199636324124972400000001605680000000131243000000049233900000006285460000003917250000008073120000000142151000000085042600000039289700000041097600000016323700000086374700000031736300000000
81970-01-01 00:00:00.00000199735414022119900000001452880000000124188000000044924500000006549870000004049290000008577550000000155957000000088320600000041586800000050041300000016897700000096160400000031619800000000
91970-01-01 00:00:00.000001998326240223899000000015031100000001270050000000409836000000063400000000027095500000090628200000001653390000000863711000000421351000000526502000000152983000000102904000000031539700000000

Last rows

yearcivil_conflictsstate_interventionconflict_between_statestotalgdp_gergdp_fragdp_itagdp_jpngdp_cangdp_rusgdp_usagdp_gbrgdp_bragdp_indgdp_mexgdp_zafgdp_chngdp_wld
221970-01-01 00:00:00.000002011307138374931000000028651600000002294990000000623315000000017933300000002045930000000155426000000002674890000000261616000000018230500000001180490000000458202000000755150000000073671500000000
231970-01-01 00:00:00.000002012239133352714000000026836700000002086960000000627236000000018283700000002208300000000161970000000002719160000000246523000000018276400000001201090000000434401000000853223000000075311600000000
241970-01-01 00:00:00.000002013279036373380000000028118800000002141920000000521233000000018466000000002292470000000167848000000002803290000000247282000000018567200000001274440000000400886000000957041000000077443200000000
251970-01-01 00:00:00.00000201428131423889090000000285596000000021620100000004896990000000180575000000020592400000001752720000000030871700000002456040000000203913000000013153500000003811990000001047570000000079575500000000
261970-01-01 00:00:00.00000201531201523357590000000243919000000018366400000004444930000000155651000000013634800000001823830000000029565700000001802210000000210359000000011718700000003467100000001106160000000075117100000000
271970-01-01 00:00:00.00000201634182543469850000000247296000000018770700000005003680000000152799000000012767900000001874510000000027228500000001795690000000229480000000010784900000003235860000001123330000000076313200000000
281970-01-01 00:00:00.00000201729211513690850000000259515000000019618000000004930840000000164927000000015742000000001954300000000026990200000002063510000000265147000000011589100000003814490000001231040000000081224600000000
291970-01-01 00:00:00.00000201831192523977290000000279096000000020919300000005036890000000172533000000016573300000002061190000000029007900000001916930000000270111000000012224100000004048420000001389480000000086274300000000
301970-01-01 00:00:00.00000201928252553888330000000272887000000020093800000005148780000000174202000000016874500000002143320000000028786700000001877820000000287050000000012694300000003879350000001427990000000087568100000000
311970-01-01 00:00:00.00000202028253563846410000000263032000000018887100000005057760000000164542000000014835000000002095300000000027598000000001444730000000266025000000010739200000003354420000001472270000000084747000000000